NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking
NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking
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DOI：
10.1515/popets-2018-0035
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发表时间：
2018-08
影响因子：
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通讯作者：
A. Shuba;A. Markopoulou;Zubair Shafiq
中科院分区：
文献类型：
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作者：
A. Shuba;A. Markopoulou;Zubair Shafiq
虽然广告是一种流行的手机应用盈利策略，但为了提高可用性、性能、隐私和安全性，屏蔽广告通常是可取的。在本文中，我们提出了NoMoAds来阻止移动设备上任何应用程序提供的广告。NoMoAds利用网络接口作为一个通用的优势点：它可以拦截、检查和阻止来自移动设备上所有应用程序的传出数据包。NoMoAds从包头和/或有效负载中提取特征，以训练机器学习分类器来检测广告请求。为了评估nomoad，我们使用EasyList和手动创建的规则收集和标记一个新的数据集。我们证明NoMoAds是有效的：它达到了高达97.8%的f分，并且在野外部署时表现良好。此外，NoMoAds能够检测出EasyList遗漏的移动广告（我们数据集中超过三分之一的广告）。我们还证明了NoMoAds是高效的：它实时地在每个数据包的基础上执行广告分类。据我们所知，NoMoAds是第一个使用机器学习方法有效地拦截所有应用程序中的广告的移动广告拦截器。
Abstract Although advertising is a popular strategy for mobile app monetization, it is often desirable to block ads in order to improve usability, performance, privacy, and security. In this paper, we propose NoMoAds to block ads served by any app on a mobile device. NoMoAds leverages the network interface as a universal vantage point: it can intercept, inspect, and block outgoing packets from all apps on a mobile device. NoMoAds extracts features from packet headers and/or payload to train machine learning classifiers for detecting ad requests. To evaluate NoMoAds, we collect and label a new dataset using both EasyList and manually created rules. We show that NoMoAds is effective: it achieves an F-score of up to 97.8% and performs well when deployed in the wild. Furthermore, NoMoAds is able to detect mobile ads that are missed by EasyList (more than one-third of ads in our dataset). We also show that NoMoAds is efficient: it performs ad classification on a per-packet basis in real-time. To the best of our knowledge, NoMoAds is the first mobile ad-blocker to effectively and efficiently block ads served across all apps using a machine learning approach.